Overview

Dataset statistics

Number of variables20
Number of observations32200183
Missing cells47159106
Missing cells (%)7.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.6 GiB
Average record size in memory153.0 B

Variable types

Numeric11
Categorical7
Boolean1
Unsupported1

Alerts

game_start_time has a high cardinality: 10524736 distinct values High cardinality
map_name has a high cardinality: 10416 distinct values High cardinality
player_login has a high cardinality: 270420 distinct values High cardinality
game_id is highly correlated with map_id and 1 other fieldsHigh correlation
game_total_players is highly correlated with player_starting_spotHigh correlation
map_id is highly correlated with game_idHigh correlation
map_width is highly correlated with map_heightHigh correlation
map_height is highly correlated with map_widthHigh correlation
player_id is highly correlated with game_idHigh correlation
player_starting_spot is highly correlated with game_total_playersHigh correlation
before_rating is highly correlated with after_ratingHigh correlation
after_rating is highly correlated with before_ratingHigh correlation
game_id is highly correlated with map_id and 1 other fieldsHigh correlation
game_total_players is highly correlated with player_starting_spotHigh correlation
map_id is highly correlated with game_idHigh correlation
map_width is highly correlated with map_heightHigh correlation
map_height is highly correlated with map_widthHigh correlation
player_id is highly correlated with game_idHigh correlation
player_starting_spot is highly correlated with game_total_playersHigh correlation
before_rating is highly correlated with after_ratingHigh correlation
after_rating is highly correlated with before_ratingHigh correlation
map_width is highly correlated with map_heightHigh correlation
map_height is highly correlated with map_widthHigh correlation
before_rating is highly correlated with after_ratingHigh correlation
after_rating is highly correlated with before_ratingHigh correlation
game_id is highly correlated with game_validity and 3 other fieldsHigh correlation
game_validity is highly correlated with game_id and 2 other fieldsHigh correlation
game_featuremod_name is highly correlated with game_validityHigh correlation
game_total_players is highly correlated with game_validity and 2 other fieldsHigh correlation
map_id is highly correlated with game_id and 2 other fieldsHigh correlation
map_width is highly correlated with map_heightHigh correlation
map_height is highly correlated with map_widthHigh correlation
player_id is highly correlated with game_id and 1 other fieldsHigh correlation
player_starting_spot is highly correlated with game_total_playersHigh correlation
player_result is highly correlated with game_id and 2 other fieldsHigh correlation
before_rating is highly correlated with after_ratingHigh correlation
after_rating is highly correlated with before_ratingHigh correlation
map_id has 2564564 (8.0%) missing values Missing
map_name has 2564564 (8.0%) missing values Missing
map_width has 2564564 (8.0%) missing values Missing
map_height has 2564564 (8.0%) missing values Missing
after_rating has 18327390 (56.9%) missing values Missing
leaderboard_ids has 18571835 (57.7%) missing values Missing
game_duration_minutes is highly skewed (γ1 = 64.35305983) Skewed
leaderboard_ids is an unsupported type, check if it needs cleaning or further analysis Unsupported
player_team has 623788 (1.9%) zeros Zeros
before_rating has 5175780 (16.1%) zeros Zeros

Reproduction

Analysis started2022-07-28 01:44:34.918806
Analysis finished2022-07-28 02:41:33.737222
Duration56 minutes and 58.82 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

game_id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10811711
Distinct (%)33.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9412086.94
Minimum535
Maximum17405720
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size245.7 MiB
2022-07-27T22:41:34.018219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum535
5-th percentile1180473
Q15362658
median9712520
Q313652441.5
95-th percentile16644375
Maximum17405720
Range17405185
Interquartile range (IQR)8289783.5

Descriptive statistics

Standard deviation4896118.822
Coefficient of variation (CV)0.5201948147
Kurtosis-1.114986037
Mean9412086.94
Median Absolute Deviation (MAD)4106763
Skewness-0.1692315475
Sum3.030709219 × 1014
Variance2.397197952 × 1013
MonotonicityNot monotonic
2022-07-27T22:41:34.090741image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1220047322
 
< 0.1%
503910917
 
< 0.1%
504843317
 
< 0.1%
537234917
 
< 0.1%
549635417
 
< 0.1%
503768317
 
< 0.1%
544427817
 
< 0.1%
547998017
 
< 0.1%
1155250016
 
< 0.1%
1279211316
 
< 0.1%
Other values (10811701)32200010
> 99.9%
ValueCountFrequency (%)
5356
< 0.1%
5363
 
< 0.1%
5474
 
< 0.1%
5492
 
< 0.1%
5522
 
< 0.1%
5556
< 0.1%
5576
< 0.1%
56212
< 0.1%
5696
< 0.1%
5736
< 0.1%
ValueCountFrequency (%)
174057201
 
< 0.1%
174057141
 
< 0.1%
174057131
 
< 0.1%
174057121
 
< 0.1%
174057111
 
< 0.1%
174057091
 
< 0.1%
174057081
 
< 0.1%
174057073
< 0.1%
174057061
 
< 0.1%
174057041
 
< 0.1%

game_start_time
Categorical

HIGH CARDINALITY

Distinct10524736
Distinct (%)32.7%
Missing0
Missing (%)0.0%
Memory size245.7 MiB
2017-05-10 20:15:44 UTC
 
15623
2020-02-16 20:35:55 UTC
 
35
2021-09-18 17:18:55 UTC
 
34
2021-10-07 21:32:19 UTC
 
34
2021-10-03 17:48:34 UTC
 
32
Other values (10524731)
32184425 

Length

Max length23
Median length23
Mean length23
Min length23

Characters and Unicode

Total characters740604209
Distinct characters16
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4211311 ?
Unique (%)13.1%

Sample

1st row2022-06-30 23:59:58 UTC
2nd row2022-06-30 23:59:44 UTC
3rd row2022-06-30 23:58:34 UTC
4th row2022-06-30 23:58:34 UTC
5th row2022-06-30 23:58:34 UTC

Common Values

ValueCountFrequency (%)
2017-05-10 20:15:44 UTC15623
 
< 0.1%
2020-02-16 20:35:55 UTC35
 
< 0.1%
2021-09-18 17:18:55 UTC34
 
< 0.1%
2021-10-07 21:32:19 UTC34
 
< 0.1%
2021-10-03 17:48:34 UTC32
 
< 0.1%
2021-10-20 19:10:50 UTC32
 
< 0.1%
2022-05-11 18:10:39 UTC32
 
< 0.1%
2020-11-19 20:03:42 UTC32
 
< 0.1%
2020-08-23 17:10:04 UTC31
 
< 0.1%
2021-10-20 19:43:37 UTC31
 
< 0.1%
Other values (10524726)32184267
> 99.9%

Length

2022-07-27T22:41:35.436352image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
utc32200183
33.3%
2020-11-2123223
 
< 0.1%
2020-11-2223175
 
< 0.1%
2022-04-1022887
 
< 0.1%
2020-12-0522639
 
< 0.1%
2020-12-0622634
 
< 0.1%
2020-11-1422597
 
< 0.1%
2020-11-2922168
 
< 0.1%
2020-11-2822125
 
< 0.1%
2020-11-0721945
 
< 0.1%
Other values (90172)64196973
66.5%

Most occurring characters

ValueCountFrequency (%)
0106976755
14.4%
297269828
13.1%
189292916
12.1%
-64400366
8.7%
64400366
8.7%
:64400366
8.7%
U32200183
 
4.3%
T32200183
 
4.3%
C32200183
 
4.3%
329990460
 
4.0%
Other values (6)127272603
17.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number450802562
60.9%
Uppercase Letter96600549
 
13.0%
Dash Punctuation64400366
 
8.7%
Space Separator64400366
 
8.7%
Other Punctuation64400366
 
8.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0106976755
23.7%
297269828
21.6%
189292916
19.8%
329990460
 
6.7%
527588247
 
6.1%
427422888
 
6.1%
818763496
 
4.2%
918719237
 
4.2%
717671426
 
3.9%
617107309
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
U32200183
33.3%
T32200183
33.3%
C32200183
33.3%
Dash Punctuation
ValueCountFrequency (%)
-64400366
100.0%
Space Separator
ValueCountFrequency (%)
64400366
100.0%
Other Punctuation
ValueCountFrequency (%)
:64400366
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common644003660
87.0%
Latin96600549
 
13.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0106976755
16.6%
297269828
15.1%
189292916
13.9%
-64400366
10.0%
64400366
10.0%
:64400366
10.0%
329990460
 
4.7%
527588247
 
4.3%
427422888
 
4.3%
818763496
 
2.9%
Other values (3)53497972
8.3%
Latin
ValueCountFrequency (%)
U32200183
33.3%
T32200183
33.3%
C32200183
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII740604209
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0106976755
14.4%
297269828
13.1%
189292916
12.1%
-64400366
8.7%
64400366
8.7%
:64400366
8.7%
U32200183
 
4.3%
T32200183
 
4.3%
C32200183
 
4.3%
329990460
 
4.0%
Other values (6)127272603
17.2%

game_duration_minutes
Real number (ℝ≥0)

SKEWED

Distinct13189
Distinct (%)< 0.1%
Missing1068
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean41.66520399
Minimum0
Maximum29847.3
Zeros8929
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size245.7 MiB
2022-07-27T22:41:35.499585image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.4
Q115.7
median34.6
Q355.9
95-th percentile101.2
Maximum29847.3
Range29847.3
Interquartile range (IQR)40.2

Descriptive statistics

Standard deviation56.67939479
Coefficient of variation (CV)1.360353229
Kurtosis17060.16284
Mean41.66520399
Median Absolute Deviation (MAD)19.9
Skewness64.35305983
Sum1341582695
Variance3212.553793
MonotonicityNot monotonic
2022-07-27T22:41:35.565303image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1292520
 
0.9%
0.8133663
 
0.4%
0.5132339
 
0.4%
0.6128726
 
0.4%
1.1122848
 
0.4%
0.7121665
 
0.4%
1.4110410
 
0.3%
0.9109132
 
0.3%
1.6104567
 
0.3%
1.9100897
 
0.3%
Other values (13179)30842348
95.8%
ValueCountFrequency (%)
08929
 
< 0.1%
0.1292520
0.9%
0.220218
 
0.1%
0.378961
 
0.2%
0.494161
 
0.3%
0.5132339
0.4%
0.6128726
0.4%
0.7121665
0.4%
0.8133663
0.4%
0.9109132
 
0.3%
ValueCountFrequency (%)
29847.31
 
< 0.1%
25822.21
 
< 0.1%
24102.88
< 0.1%
20199.31
 
< 0.1%
20100.21
 
< 0.1%
166671
 
< 0.1%
14541.21
 
< 0.1%
14539.21
 
< 0.1%
12975.512
< 0.1%
11954.31
 
< 0.1%

game_validity
Categorical

HIGH CORRELATION

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size245.7 MiB
VALID
14589108 
TOO_MANY_DESYNCS
3255349 
UNKNOWN_RESULT
2738617 
BAD_MOD
2502835 
TOO_SHORT
1682861 
Other values (18)
7431413 

Length

Max length23
Median length21
Mean length9.485424601
Min length5

Characters and Unicode

Total characters305432408
Distinct characters24
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUNKNOWN_RESULT
2nd rowCOOP_NOT_RANKED
3rd rowVALID
4th rowVALID
5th rowVALID

Common Values

ValueCountFrequency (%)
VALID14589108
45.3%
TOO_MANY_DESYNCS3255349
 
10.1%
UNKNOWN_RESULT2738617
 
8.5%
BAD_MOD2502835
 
7.8%
TOO_SHORT1682861
 
5.2%
BAD_UNIT_RESTRICTIONS1415966
 
4.4%
HAS_AI1327815
 
4.1%
SINGLE_PLAYER1007129
 
3.1%
UNEVEN_TEAMS_NOT_RANKED892808
 
2.8%
BAD_MAP718731
 
2.2%
Other values (13)2068964
 
6.4%

Length

2022-07-27T22:41:35.630090image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
valid14589108
45.3%
too_many_desyncs3255349
 
10.1%
unknown_result2738617
 
8.5%
bad_mod2502835
 
7.8%
too_short1682861
 
5.2%
bad_unit_restrictions1415966
 
4.4%
has_ai1327815
 
4.1%
single_player1007129
 
3.1%
uneven_teams_not_ranked892808
 
2.8%
bad_map718731
 
2.2%
Other values (13)2068964
 
6.4%

Most occurring characters

ValueCountFrequency (%)
A31178518
 
10.2%
D27826304
 
9.1%
N25856667
 
8.5%
_25463095
 
8.3%
I22766781
 
7.5%
O22354437
 
7.3%
L20332707
 
6.7%
T17733311
 
5.8%
S17594817
 
5.8%
V15825827
 
5.2%
Other values (14)78499944
25.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter279969313
91.7%
Connector Punctuation25463095
 
8.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A31178518
11.1%
D27826304
9.9%
N25856667
9.2%
I22766781
 
8.1%
O22354437
 
8.0%
L20332707
 
7.3%
T17733311
 
6.3%
S17594817
 
6.3%
V15825827
 
5.7%
E15533957
 
5.5%
Other values (13)62965987
22.5%
Connector Punctuation
ValueCountFrequency (%)
_25463095
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin279969313
91.7%
Common25463095
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A31178518
11.1%
D27826304
9.9%
N25856667
9.2%
I22766781
 
8.1%
O22354437
 
8.0%
L20332707
 
7.3%
T17733311
 
6.3%
S17594817
 
6.3%
V15825827
 
5.7%
E15533957
 
5.5%
Other values (13)62965987
22.5%
Common
ValueCountFrequency (%)
_25463095
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII305432408
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A31178518
 
10.2%
D27826304
 
9.1%
N25856667
 
8.5%
_25463095
 
8.3%
I22766781
 
7.5%
O22354437
 
7.3%
L20332707
 
6.7%
T17733311
 
5.8%
S17594817
 
5.8%
V15825827
 
5.2%
Other values (14)78499944
25.7%

game_featuremod_name
Categorical

HIGH CORRELATION

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size245.7 MiB
FAF
26326332 
Ladder1v1
 
2286435
Coop
 
1897527
Nomads
 
552158
Phantom-X
 
353433
Other values (10)
 
784298

Length

Max length16
Median length3
Mean length3.830369349
Min length3

Characters and Unicode

Total characters123338594
Distinct characters40
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFAF
2nd rowCoop
3rd rowFAF
4th rowFAF
5th rowFAF

Common Values

ValueCountFrequency (%)
FAF26326332
81.8%
Ladder1v12286435
 
7.1%
Coop1897527
 
5.9%
Nomads552158
 
1.7%
Phantom-X353433
 
1.1%
Xtreme Wars348758
 
1.1%
FAF Beta Balance202126
 
0.6%
FAF Develop87119
 
0.3%
Equilibrium47860
 
0.1%
Murder Party33760
 
0.1%
Other values (5)64675
 
0.2%

Length

2022-07-27T22:41:35.691537image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
faf26615577
80.4%
ladder1v12286435
 
6.9%
coop1897527
 
5.7%
nomads552158
 
1.7%
phantom-x353433
 
1.1%
xtreme348758
 
1.1%
wars348758
 
1.1%
beta202126
 
0.6%
balance202126
 
0.6%
develop87119
 
0.3%
Other values (11)217795
 
0.7%

Most occurring characters

ValueCountFrequency (%)
F53231154
43.2%
A26637034
21.6%
d5161480
 
4.2%
o4852048
 
3.9%
14572870
 
3.7%
a4260963
 
3.5%
e3608781
 
2.9%
r3179054
 
2.6%
v2373554
 
1.9%
L2307892
 
1.9%
Other values (30)13153764
 
10.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter86714153
70.3%
Lowercase Letter30786509
 
25.0%
Decimal Number4572870
 
3.7%
Space Separator911629
 
0.7%
Dash Punctuation353433
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d5161480
16.8%
o4852048
15.8%
a4260963
13.8%
e3608781
11.7%
r3179054
10.3%
v2373554
7.7%
p2009152
 
6.5%
m1304901
 
4.2%
t975163
 
3.2%
s946879
 
3.1%
Other values (12)2114534
6.9%
Uppercase Letter
ValueCountFrequency (%)
F53231154
61.4%
A26637034
30.7%
L2307892
 
2.7%
C1922033
 
2.2%
X702191
 
0.8%
N552158
 
0.6%
B425709
 
0.5%
P387193
 
0.4%
W348758
 
0.4%
D89811
 
0.1%
Other values (5)110220
 
0.1%
Decimal Number
ValueCountFrequency (%)
14572870
100.0%
Space Separator
ValueCountFrequency (%)
911629
100.0%
Dash Punctuation
ValueCountFrequency (%)
-353433
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin117500662
95.3%
Common5837932
 
4.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
F53231154
45.3%
A26637034
22.7%
d5161480
 
4.4%
o4852048
 
4.1%
a4260963
 
3.6%
e3608781
 
3.1%
r3179054
 
2.7%
v2373554
 
2.0%
L2307892
 
2.0%
p2009152
 
1.7%
Other values (27)9879550
 
8.4%
Common
ValueCountFrequency (%)
14572870
78.3%
911629
 
15.6%
-353433
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII123338594
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F53231154
43.2%
A26637034
21.6%
d5161480
 
4.2%
o4852048
 
3.9%
14572870
 
3.7%
a4260963
 
3.5%
e3608781
 
2.9%
r3179054
 
2.6%
v2373554
 
1.9%
L2307892
 
1.9%
Other values (30)13153764
 
10.7%

game_total_players
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9791081
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size245.7 MiB
2022-07-27T22:41:35.743946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median6
Q38
95-th percentile12
Maximum22
Range21
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.045415817
Coefficient of variation (CV)0.676591851
Kurtosis-1.233475966
Mean5.9791081
Median Absolute Deviation (MAD)4
Skewness0.3124295499
Sum192528375
Variance16.36538913
MonotonicityNot monotonic
2022-07-27T22:41:42.527625image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
86709296
20.8%
26424048
20.0%
125615220
17.4%
14454367
13.8%
42324252
 
7.2%
62302668
 
7.2%
31736388
 
5.4%
101606660
 
5.0%
5355425
 
1.1%
16210592
 
0.7%
Other values (8)461267
 
1.4%
ValueCountFrequency (%)
14454367
13.8%
26424048
20.0%
31736388
 
5.4%
42324252
 
7.2%
5355425
 
1.1%
62302668
 
7.2%
7183337
 
0.6%
86709296
20.8%
984348
 
0.3%
101606660
 
5.0%
ValueCountFrequency (%)
2222
 
< 0.1%
17119
 
< 0.1%
16210592
 
0.7%
158880
 
< 0.1%
14136710
 
0.4%
137514
 
< 0.1%
125615220
17.4%
1140337
 
0.1%
101606660
 
5.0%
984348
 
0.3%

map_id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct19024
Distinct (%)0.1%
Missing2564564
Missing (%)8.0%
Infinite0
Infinite (%)0.0%
Mean5935.585321
Minimum1
Maximum22655
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size245.7 MiB
2022-07-27T22:41:42.583253image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile156
Q1566
median4704
Q38972
95-th percentile17709
Maximum22655
Range22654
Interquartile range (IQR)8406

Descriptive statistics

Standard deviation5856.162519
Coefficient of variation (CV)0.9866192132
Kurtosis0.03161374621
Mean5935.585321
Median Absolute Deviation (MAD)4138
Skewness0.9641361308
Sum1.759047451 × 1011
Variance34294639.45
MonotonicityNot monotonic
2022-07-27T22:41:42.640243image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5602615634
 
8.1%
155451135800
 
3.5%
246920487
 
2.9%
3658832120
 
2.6%
3660737759
 
2.3%
21131690359
 
2.1%
10061668616
 
2.1%
566448505
 
1.4%
9698400403
 
1.2%
558305865
 
0.9%
Other values (19014)20880071
64.8%
(Missing)2564564
 
8.0%
ValueCountFrequency (%)
113813
 
< 0.1%
210393
 
< 0.1%
3405
 
< 0.1%
46620
 
< 0.1%
5105276
0.3%
6295
 
< 0.1%
72967
 
< 0.1%
875604
0.2%
95096
 
< 0.1%
11121
 
< 0.1%
ValueCountFrequency (%)
226552
 
< 0.1%
226549
 
< 0.1%
226531
 
< 0.1%
226526
 
< 0.1%
226511
 
< 0.1%
226479
 
< 0.1%
2264319
 
< 0.1%
2264210
 
< 0.1%
2264112
 
< 0.1%
22640194
< 0.1%

map_name
Categorical

HIGH CARDINALITY
MISSING

Distinct10416
Distinct (%)< 0.1%
Missing2564564
Missing (%)8.0%
Memory size245.7 MiB
Seton's Clutch
2617077 
DualGap Adaptive
 
2006053
gap of rohan
 
920487
DualGap_fix_adaptive
 
883097
Astro Crater Battles
 
832120
Other values (10411)
22376785 

Length

Max length84
Median length49
Mean length17.39278501
Min length1

Characters and Unicode

Total characters515445950
Distinct characters84
Distinct categories12 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique177 ?
Unique (%)< 0.1%

Sample

1st rowHold the line
2nd rowDesert Gap
3rd rowDesert Gap
4th rowDesert Gap
5th rowDesert Gap

Common Values

ValueCountFrequency (%)
Seton's Clutch2617077
 
8.1%
DualGap Adaptive2006053
 
6.2%
gap of rohan920487
 
2.9%
DualGap_fix_adaptive883097
 
2.7%
Astro Crater Battles832120
 
2.6%
Astro Crater Battles 4x4 v2754646
 
2.3%
Fields of Isis448505
 
1.4%
Astro Crater Battles 4x4 rich_v2400403
 
1.2%
Open Palms305865
 
0.9%
Twin Rivers293313
 
0.9%
Other values (10406)20174053
62.7%
(Missing)2564564
 
8.0%

Length

2022-07-27T22:41:42.732793image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
crater3180320
 
3.9%
astro3146680
 
3.8%
adaptive2885258
 
3.5%
battles2850698
 
3.5%
clutch2841900
 
3.5%
of2835426
 
3.5%
seton's2654494
 
3.2%
dualgap2225553
 
2.7%
gap2102836
 
2.6%
4x41776719
 
2.2%
Other values (7254)55241004
67.6%

Most occurring characters

ValueCountFrequency (%)
52382117
 
10.2%
a44768237
 
8.7%
e35489075
 
6.9%
t33157370
 
6.4%
s26851623
 
5.2%
o25505555
 
4.9%
r25399317
 
4.9%
l23304820
 
4.5%
i22016034
 
4.3%
n18719516
 
3.6%
Other values (74)207852286
40.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter354596443
68.8%
Uppercase Letter72803204
 
14.1%
Space Separator52382117
 
10.2%
Decimal Number23000960
 
4.5%
Connector Punctuation7023418
 
1.4%
Other Punctuation4529251
 
0.9%
Dash Punctuation998338
 
0.2%
Open Punctuation53152
 
< 0.1%
Close Punctuation52968
 
< 0.1%
Math Symbol5876
 
< 0.1%
Other values (2)223
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a44768237
12.6%
e35489075
10.0%
t33157370
 
9.4%
s26851623
 
7.6%
o25505555
 
7.2%
r25399317
 
7.2%
l23304820
 
6.6%
i22016034
 
6.2%
n18719516
 
5.3%
v15832110
 
4.5%
Other values (16)83552786
23.6%
Uppercase Letter
ValueCountFrequency (%)
A8514460
11.7%
C8398273
11.5%
S6850281
9.4%
D6142574
 
8.4%
G5328186
 
7.3%
B5236233
 
7.2%
P5004666
 
6.9%
F3802549
 
5.2%
T3663782
 
5.0%
R3524706
 
4.8%
Other values (16)16337494
22.4%
Decimal Number
ValueCountFrequency (%)
45938207
25.8%
24459873
19.4%
13235239
14.1%
53123757
13.6%
02359533
 
10.3%
31893312
 
8.2%
61094216
 
4.8%
8419973
 
1.8%
7329065
 
1.4%
9147785
 
0.6%
Other Punctuation
ValueCountFrequency (%)
'3313931
73.2%
.1102756
 
24.3%
&56403
 
1.2%
!32059
 
0.7%
,12464
 
0.3%
;10390
 
0.2%
\1151
 
< 0.1%
:96
 
< 0.1%
/1
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
+3648
62.1%
>1097
 
18.7%
<1097
 
18.7%
|34
 
0.6%
Open Punctuation
ValueCountFrequency (%)
(46052
86.6%
[7100
 
13.4%
Close Punctuation
ValueCountFrequency (%)
)45868
86.6%
]7100
 
13.4%
Space Separator
ValueCountFrequency (%)
52382117
100.0%
Connector Punctuation
ValueCountFrequency (%)
_7023418
100.0%
Dash Punctuation
ValueCountFrequency (%)
-998338
100.0%
Modifier Symbol
ValueCountFrequency (%)
`195
100.0%
Control
ValueCountFrequency (%)
28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin427399647
82.9%
Common88046303
 
17.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a44768237
 
10.5%
e35489075
 
8.3%
t33157370
 
7.8%
s26851623
 
6.3%
o25505555
 
6.0%
r25399317
 
5.9%
l23304820
 
5.5%
i22016034
 
5.2%
n18719516
 
4.4%
v15832110
 
3.7%
Other values (42)156355990
36.6%
Common
ValueCountFrequency (%)
52382117
59.5%
_7023418
 
8.0%
45938207
 
6.7%
24459873
 
5.1%
'3313931
 
3.8%
13235239
 
3.7%
53123757
 
3.5%
02359533
 
2.7%
31893312
 
2.2%
.1102756
 
1.3%
Other values (22)3214160
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII515445950
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
52382117
 
10.2%
a44768237
 
8.7%
e35489075
 
6.9%
t33157370
 
6.4%
s26851623
 
5.2%
o25505555
 
4.9%
r25399317
 
4.9%
l23304820
 
4.5%
i22016034
 
4.3%
n18719516
 
3.6%
Other values (74)207852286
40.3%

map_width
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct9
Distinct (%)< 0.1%
Missing2564564
Missing (%)8.0%
Infinite0
Infinite (%)0.0%
Mean749.9935907
Minimum10
Maximum4096
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size245.7 MiB
2022-07-27T22:41:42.782786image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile256
Q1512
median512
Q31024
95-th percentile1024
Maximum4096
Range4086
Interquartile range (IQR)512

Descriptive statistics

Standard deviation455.0894438
Coefficient of variation (CV)0.6067911106
Kurtosis23.7068909
Mean749.9935907
Median Absolute Deviation (MAD)0
Skewness3.847492419
Sum2.222652431 × 1010
Variance207106.4019
MonotonicityNot monotonic
2022-07-27T22:41:42.826646image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
51215899183
49.4%
102410953376
34.0%
2561858802
 
5.8%
2048660228
 
2.1%
4096254095
 
0.8%
1287459
 
< 0.1%
642457
 
< 0.1%
1017
 
< 0.1%
202
 
< 0.1%
(Missing)2564564
 
8.0%
ValueCountFrequency (%)
1017
 
< 0.1%
202
 
< 0.1%
642457
 
< 0.1%
1287459
 
< 0.1%
2561858802
 
5.8%
51215899183
49.4%
102410953376
34.0%
2048660228
 
2.1%
4096254095
 
0.8%
ValueCountFrequency (%)
4096254095
 
0.8%
2048660228
 
2.1%
102410953376
34.0%
51215899183
49.4%
2561858802
 
5.8%
1287459
 
< 0.1%
642457
 
< 0.1%
202
 
< 0.1%
1017
 
< 0.1%

map_height
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct11
Distinct (%)< 0.1%
Missing2564564
Missing (%)8.0%
Infinite0
Infinite (%)0.0%
Mean747.4014564
Minimum10
Maximum4096
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size245.7 MiB
2022-07-27T22:41:42.872860image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile256
Q1512
median512
Q31024
95-th percentile1024
Maximum4096
Range4086
Interquartile range (IQR)512

Descriptive statistics

Standard deviation454.1259459
Coefficient of variation (CV)0.6076064503
Kurtosis23.86034892
Mean747.4014564
Median Absolute Deviation (MAD)0
Skewness3.859458393
Sum2.21497048 × 1010
Variance206230.3747
MonotonicityNot monotonic
2022-07-27T22:41:42.915618image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
51215965512
49.6%
102410861671
33.7%
2561892465
 
5.9%
2048650577
 
2.0%
4096252667
 
0.8%
646615
 
< 0.1%
1285982
 
< 0.1%
400109
 
< 0.1%
1017
 
< 0.1%
9602
 
< 0.1%
(Missing)2564564
 
8.0%
ValueCountFrequency (%)
1017
 
< 0.1%
202
 
< 0.1%
646615
 
< 0.1%
1285982
 
< 0.1%
2561892465
 
5.9%
400109
 
< 0.1%
51215965512
49.6%
9602
 
< 0.1%
102410861671
33.7%
2048650577
 
2.0%
ValueCountFrequency (%)
4096252667
 
0.8%
2048650577
 
2.0%
102410861671
33.7%
9602
 
< 0.1%
51215965512
49.6%
400109
 
< 0.1%
2561892465
 
5.9%
1285982
 
< 0.1%
646615
 
< 0.1%
202
 
< 0.1%

player_id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct270421
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean191303.4255
Minimum5
Maximum449760
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size245.7 MiB
2022-07-27T22:41:42.977957image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile14746
Q176850
median186952
Q3298017
95-th percentile399806
Maximum449760
Range449755
Interquartile range (IQR)221167

Descriptive statistics

Standard deviation126030.3201
Coefficient of variation (CV)0.6587980313
Kurtosis-1.14843654
Mean191303.4255
Median Absolute Deviation (MAD)110479
Skewness0.1976396293
Sum6.160005311 × 1012
Variance1.588364159 × 1010
MonotonicityNot monotonic
2022-07-27T22:41:43.034454image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11655823601
 
0.1%
6532719063
 
0.1%
2274517156
 
0.1%
5067917073
 
0.1%
36116290
 
0.1%
1449215002
 
< 0.1%
7353214357
 
< 0.1%
3443414019
 
< 0.1%
2123413236
 
< 0.1%
264213171
 
< 0.1%
Other values (270411)32037215
99.5%
ValueCountFrequency (%)
5625
 
< 0.1%
6187
 
< 0.1%
77
 
< 0.1%
933
 
< 0.1%
1026
 
< 0.1%
201
 
< 0.1%
221263
 
< 0.1%
233452
< 0.1%
244854
< 0.1%
2556
 
< 0.1%
ValueCountFrequency (%)
4497601
 
< 0.1%
4497582
< 0.1%
4497561
 
< 0.1%
4497551
 
< 0.1%
4497541
 
< 0.1%
4497531
 
< 0.1%
4497521
 
< 0.1%
4497491
 
< 0.1%
4497472
< 0.1%
4497443
< 0.1%

player_login
Categorical

HIGH CARDINALITY

Distinct270420
Distinct (%)0.8%
Missing557
Missing (%)< 0.1%
Memory size245.7 MiB
coca
 
23601
Photon
 
19063
Chosen
 
17156
DDDX
 
17073
Warner-Valholl
 
16290
Other values (270415)
32106443 

Length

Max length20
Median length17
Mean length8.650968803
Min length1

Characters and Unicode

Total characters278557960
Distinct characters66
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27753 ?
Unique (%)0.1%

Sample

1st rowWalgreens
2nd rowStarcruiser
3rd rowourubapu
4th rowEnzord_Rocka
5th rowDefiant

Common Values

ValueCountFrequency (%)
coca23601
 
0.1%
Photon19063
 
0.1%
Chosen17156
 
0.1%
DDDX17073
 
0.1%
Warner-Valholl16290
 
0.1%
alexanderberlin15002
 
< 0.1%
K24014357
 
< 0.1%
HighLander-TX14019
 
< 0.1%
dardan13236
 
< 0.1%
nemir13171
 
< 0.1%
Other values (270410)32036658
99.5%

Length

2022-07-27T22:41:43.285593image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
coca23601
 
0.1%
photon19063
 
0.1%
chosen17156
 
0.1%
dddx17073
 
0.1%
warner-valholl16290
 
0.1%
alexanderberlin15002
 
< 0.1%
k24014357
 
< 0.1%
highlander-tx14019
 
< 0.1%
dardan13236
 
< 0.1%
nemir13171
 
< 0.1%
Other values (269293)32041332
99.5%

Most occurring characters

ValueCountFrequency (%)
a22075532
 
7.9%
e22061727
 
7.9%
o17886907
 
6.4%
r17515944
 
6.3%
i15684231
 
5.6%
n14090013
 
5.1%
t11158434
 
4.0%
l11034672
 
4.0%
s10550598
 
3.8%
u7474469
 
2.7%
Other values (56)129025433
46.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter210405473
75.5%
Uppercase Letter47656981
 
17.1%
Decimal Number15404767
 
5.5%
Connector Punctuation4135947
 
1.5%
Dash Punctuation949941
 
0.3%
Space Separator4674
 
< 0.1%
Other Punctuation177
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a22075532
 
10.5%
e22061727
 
10.5%
o17886907
 
8.5%
r17515944
 
8.3%
i15684231
 
7.5%
n14090013
 
6.7%
t11158434
 
5.3%
l11034672
 
5.2%
s10550598
 
5.0%
u7474469
 
3.6%
Other values (16)60872946
28.9%
Uppercase Letter
ValueCountFrequency (%)
S4267631
 
9.0%
A3303587
 
6.9%
M3055686
 
6.4%
T2906101
 
6.1%
D2858187
 
6.0%
R2511761
 
5.3%
B2500258
 
5.2%
C2439644
 
5.1%
N2106631
 
4.4%
P2027852
 
4.3%
Other values (16)19679643
41.3%
Decimal Number
ValueCountFrequency (%)
12821892
18.3%
02351878
15.3%
22001556
13.0%
31574562
10.2%
41285602
8.3%
91258431
8.2%
71183640
7.7%
81012914
 
6.6%
51002128
 
6.5%
6912164
 
5.9%
Connector Punctuation
ValueCountFrequency (%)
_4135947
100.0%
Dash Punctuation
ValueCountFrequency (%)
-949941
100.0%
Space Separator
ValueCountFrequency (%)
4674
100.0%
Other Punctuation
ValueCountFrequency (%)
.177
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin258062454
92.6%
Common20495506
 
7.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a22075532
 
8.6%
e22061727
 
8.5%
o17886907
 
6.9%
r17515944
 
6.8%
i15684231
 
6.1%
n14090013
 
5.5%
t11158434
 
4.3%
l11034672
 
4.3%
s10550598
 
4.1%
u7474469
 
2.9%
Other values (42)108529927
42.1%
Common
ValueCountFrequency (%)
_4135947
20.2%
12821892
13.8%
02351878
11.5%
22001556
9.8%
31574562
 
7.7%
41285602
 
6.3%
91258431
 
6.1%
71183640
 
5.8%
81012914
 
4.9%
51002128
 
4.9%
Other values (4)1866956
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII278557960
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a22075532
 
7.9%
e22061727
 
7.9%
o17886907
 
6.4%
r17515944
 
6.3%
i15684231
 
5.6%
n14090013
 
5.1%
t11158434
 
4.0%
l11034672
 
4.0%
s10550598
 
3.8%
u7474469
 
2.7%
Other values (56)129025433
46.3%

player_faction
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size245.7 MiB
UEF
10865422 
Cybran
9133088 
Aeon
5710582 
Seraphim
4497824 
Other
1993267 

Length

Max length8
Median length6
Mean length4.85047085
Min length3

Characters and Unicode

Total characters156186049
Distinct characters19
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUEF
2nd rowCybran
3rd rowUEF
4th rowUEF
5th rowUEF

Common Values

ValueCountFrequency (%)
UEF10865422
33.7%
Cybran9133088
28.4%
Aeon5710582
17.7%
Seraphim4497824
14.0%
Other1993267
 
6.2%

Length

2022-07-27T22:41:43.338072image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-27T22:41:43.395636image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
uef10865422
33.7%
cybran9133088
28.4%
aeon5710582
17.7%
seraphim4497824
14.0%
other1993267
 
6.2%

Most occurring characters

ValueCountFrequency (%)
r15624179
 
10.0%
n14843670
 
9.5%
a13630912
 
8.7%
e12201673
 
7.8%
U10865422
 
7.0%
F10865422
 
7.0%
E10865422
 
7.0%
C9133088
 
5.8%
y9133088
 
5.8%
b9133088
 
5.8%
Other values (9)39890085
25.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter102255022
65.5%
Uppercase Letter53931027
34.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r15624179
15.3%
n14843670
14.5%
a13630912
13.3%
e12201673
11.9%
y9133088
8.9%
b9133088
8.9%
h6491091
6.3%
o5710582
 
5.6%
p4497824
 
4.4%
i4497824
 
4.4%
Other values (2)6491091
6.3%
Uppercase Letter
ValueCountFrequency (%)
U10865422
20.1%
F10865422
20.1%
E10865422
20.1%
C9133088
16.9%
A5710582
10.6%
S4497824
8.3%
O1993267
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
Latin156186049
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r15624179
 
10.0%
n14843670
 
9.5%
a13630912
 
8.7%
e12201673
 
7.8%
U10865422
 
7.0%
F10865422
 
7.0%
E10865422
 
7.0%
C9133088
 
5.8%
y9133088
 
5.8%
b9133088
 
5.8%
Other values (9)39890085
25.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII156186049
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r15624179
 
10.0%
n14843670
 
9.5%
a13630912
 
8.7%
e12201673
 
7.8%
U10865422
 
7.0%
F10865422
 
7.0%
E10865422
 
7.0%
C9133088
 
5.8%
y9133088
 
5.8%
b9133088
 
5.8%
Other values (9)39890085
25.5%

player_team
Real number (ℝ)

ZEROS

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.002442471
Minimum-1
Maximum14
Zeros623788
Zeros (%)1.9%
Negative8
Negative (%)< 0.1%
Memory size245.7 MiB
2022-07-27T22:41:43.440324image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q11
median2
Q33
95-th percentile3
Maximum14
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.8897006936
Coefficient of variation (CV)0.4443077425
Kurtosis4.528491626
Mean2.002442471
Median Absolute Deviation (MAD)1
Skewness0.8715270006
Sum64479014
Variance0.7915673242
MonotonicityNot monotonic
2022-07-27T22:41:43.481644image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
213759369
42.7%
18747230
27.2%
38650787
26.9%
0623788
 
1.9%
4143234
 
0.4%
5100843
 
0.3%
677251
 
0.2%
775818
 
0.2%
810792
 
< 0.1%
98393
 
< 0.1%
Other values (6)2678
 
< 0.1%
ValueCountFrequency (%)
-18
 
< 0.1%
0623788
 
1.9%
18747230
27.2%
213759369
42.7%
38650787
26.9%
4143234
 
0.4%
5100843
 
0.3%
677251
 
0.2%
775818
 
0.2%
810792
 
< 0.1%
ValueCountFrequency (%)
141
 
< 0.1%
138
 
< 0.1%
12168
 
< 0.1%
11373
 
< 0.1%
102120
 
< 0.1%
98393
 
< 0.1%
810792
 
< 0.1%
775818
0.2%
677251
0.2%
5100843
0.3%

player_starting_spot
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct30
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.884059355
Minimum0
Maximum32
Zeros10
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size245.7 MiB
2022-07-27T22:41:43.533869image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median3
Q36
95-th percentile10
Maximum32
Range32
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.977006983
Coefficient of variation (CV)0.7664679428
Kurtosis0.3707809682
Mean3.884059355
Median Absolute Deviation (MAD)2
Skewness1.035193478
Sum125067422
Variance8.862570579
MonotonicityNot monotonic
2022-07-27T22:41:43.580153image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
18851231
27.5%
25706807
17.7%
33488108
 
10.8%
43013535
 
9.4%
52454296
 
7.6%
62297720
 
7.1%
71866976
 
5.8%
81767178
 
5.5%
9774804
 
2.4%
10725738
 
2.3%
Other values (20)1253790
 
3.9%
ValueCountFrequency (%)
010
 
< 0.1%
18851231
27.5%
25706807
17.7%
33488108
 
10.8%
43013535
 
9.4%
52454296
 
7.6%
62297720
 
7.1%
71866976
 
5.8%
81767178
 
5.5%
9774804
 
2.4%
ValueCountFrequency (%)
321
 
< 0.1%
315
< 0.1%
301
 
< 0.1%
291
 
< 0.1%
281
 
< 0.1%
271
 
< 0.1%
261
 
< 0.1%
241
 
< 0.1%
221
 
< 0.1%
201
 
< 0.1%

player_result
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size245.7 MiB
UNKNOWN
22531157 
DEFEAT
5598605 
VICTORY
3924193 
CONFLICTING
 
142686
DRAW
 
3542

Length

Max length11
Median length7
Mean length6.843526138
Min length4

Characters and Unicode

Total characters220362794
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUNKNOWN
2nd rowUNKNOWN
3rd rowDEFEAT
4th rowDEFEAT
5th rowDEFEAT

Common Values

ValueCountFrequency (%)
UNKNOWN22531157
70.0%
DEFEAT5598605
 
17.4%
VICTORY3924193
 
12.2%
CONFLICTING142686
 
0.4%
DRAW3542
 
< 0.1%

Length

2022-07-27T22:41:43.630532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-27T22:41:43.682298image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
unknown22531157
70.0%
defeat5598605
 
17.4%
victory3924193
 
12.2%
conflicting142686
 
0.4%
draw3542
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N67878843
30.8%
O26598036
 
12.1%
W22534699
 
10.2%
U22531157
 
10.2%
K22531157
 
10.2%
E11197210
 
5.1%
T9665484
 
4.4%
F5741291
 
2.6%
A5602147
 
2.5%
D5602147
 
2.5%
Other values (7)20480623
 
9.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter220362794
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N67878843
30.8%
O26598036
 
12.1%
W22534699
 
10.2%
U22531157
 
10.2%
K22531157
 
10.2%
E11197210
 
5.1%
T9665484
 
4.4%
F5741291
 
2.6%
A5602147
 
2.5%
D5602147
 
2.5%
Other values (7)20480623
 
9.3%

Most occurring scripts

ValueCountFrequency (%)
Latin220362794
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N67878843
30.8%
O26598036
 
12.1%
W22534699
 
10.2%
U22531157
 
10.2%
K22531157
 
10.2%
E11197210
 
5.1%
T9665484
 
4.4%
F5741291
 
2.6%
A5602147
 
2.5%
D5602147
 
2.5%
Other values (7)20480623
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII220362794
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N67878843
30.8%
O26598036
 
12.1%
W22534699
 
10.2%
U22531157
 
10.2%
K22531157
 
10.2%
E11197210
 
5.1%
T9665484
 
4.4%
F5741291
 
2.6%
A5602147
 
2.5%
D5602147
 
2.5%
Other values (7)20480623
 
9.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size30.7 MiB
False
32198355 
True
 
1828
ValueCountFrequency (%)
False32198355
> 99.9%
True1828
 
< 0.1%
2022-07-27T22:41:43.729821image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

before_rating
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct32875
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean698.0673791
Minimum-6146
Maximum49730
Zeros5175780
Zeros (%)16.1%
Negative1438957
Negative (%)4.5%
Memory size245.7 MiB
2022-07-27T22:41:43.775704image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-6146
5-th percentile0
Q1227.5
median741.2
Q31071.4
95-th percentile1553.8
Maximum49730
Range55876
Interquartile range (IQR)843.9

Descriptive statistics

Standard deviation527.747313
Coefficient of variation (CV)0.7560119966
Kurtosis8.53581572
Mean698.0673791
Median Absolute Deviation (MAD)391.6
Skewness0.2102912825
Sum2.247789735 × 1010
Variance278517.2264
MonotonicityNot monotonic
2022-07-27T22:41:43.831246image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05175780
 
16.1%
477.961239
 
0.2%
-53.150703
 
0.2%
-54.623814
 
0.1%
472.923226
 
0.1%
343.721154
 
0.1%
704.112498
 
< 0.1%
337.612037
 
< 0.1%
-44.711436
 
< 0.1%
1142.49729
 
< 0.1%
Other values (32865)26798567
83.2%
ValueCountFrequency (%)
-61461
 
< 0.1%
-15007
< 0.1%
-1201.816
< 0.1%
-1161.21
 
< 0.1%
-1122.58
< 0.1%
-1081.62
 
< 0.1%
-1072.11
 
< 0.1%
-1070.71
 
< 0.1%
-1020.49
< 0.1%
-1018.61
 
< 0.1%
ValueCountFrequency (%)
497301
 
< 0.1%
497002
< 0.1%
496821
 
< 0.1%
3056.93
< 0.1%
3056.81
 
< 0.1%
30501
 
< 0.1%
3047.51
 
< 0.1%
3046.42
< 0.1%
3046.33
< 0.1%
3045.71
 
< 0.1%

after_rating
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct33040
Distinct (%)0.2%
Missing18327390
Missing (%)56.9%
Infinite0
Infinite (%)0.0%
Mean915.8573469
Minimum-10013.6
Maximum49735.8
Zeros165
Zeros (%)< 0.1%
Negative393198
Negative (%)1.2%
Memory size245.7 MiB
2022-07-27T22:41:43.892298image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-10013.6
5-th percentile144.9
Q1636.1
median920.6
Q31206
95-th percentile1654.9
Maximum49735.8
Range59749.4
Interquartile range (IQR)569.9

Descriptive statistics

Standard deviation448.5193635
Coefficient of variation (CV)0.4897262277
Kurtosis40.55911793
Mean915.8573469
Median Absolute Deviation (MAD)284.9
Skewness0.3065184828
Sum1.270549939 × 1010
Variance201169.6195
MonotonicityNot monotonic
2022-07-27T22:41:43.946230image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
477.96402
 
< 0.1%
-53.15939
 
< 0.1%
472.92897
 
< 0.1%
704.12434
 
< 0.1%
-54.62411
 
< 0.1%
839.21815
 
< 0.1%
930.91680
 
< 0.1%
699.61653
 
< 0.1%
343.71613
 
< 0.1%
835.91566
 
< 0.1%
Other values (33030)13844383
43.0%
(Missing)18327390
56.9%
ValueCountFrequency (%)
-10013.61
< 0.1%
-5593.61
< 0.1%
-19371
< 0.1%
-1630.91
< 0.1%
-1582.11
< 0.1%
-1580.71
< 0.1%
-1526.51
< 0.1%
-1410.91
< 0.1%
-1388.21
< 0.1%
-1356.51
< 0.1%
ValueCountFrequency (%)
49735.81
< 0.1%
497301
< 0.1%
49694.81
< 0.1%
496821
< 0.1%
3056.91
< 0.1%
3056.81
< 0.1%
30501
< 0.1%
3047.51
< 0.1%
3046.41
< 0.1%
3046.31
< 0.1%

leaderboard_ids
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing18571835
Missing (%)57.7%
Memory size245.7 MiB

Interactions

2022-07-27T22:34:19.815353image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:25:27.633438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:26:18.101142image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:27:09.885386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:28:04.507758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:28:57.037171image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:29:48.638976image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:30:41.175076image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:31:38.829074image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:32:35.330191image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:33:32.157784image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:34:21.922732image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:25:32.999804image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:26:22.992177image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:27:15.422519image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:28:09.429444image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:29:02.054075image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:29:53.612091image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:30:47.240567image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:31:45.081466image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:32:41.200330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:33:37.254568image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:34:24.079286image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:25:37.469869image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:26:28.054136image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:27:20.148382image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:28:14.416580image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:29:06.787197image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:29:58.520290image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:30:52.705481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:31:50.442682image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:32:46.411002image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:33:41.883539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:34:26.138056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:25:42.247544image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:26:32.585963image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:27:25.684693image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:28:19.153029image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:29:11.760672image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:30:03.384731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:30:58.131007image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:31:55.785494image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:32:51.786386image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:33:46.506834image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:34:28.198048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:25:46.896258image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:26:37.177748image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:27:30.741893image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:28:23.991361image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:29:16.373806image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:30:08.218019image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:31:03.677121image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:32:01.162692image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:32:57.229349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:33:51.315537image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:34:30.271310image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:25:51.513068image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:26:41.668427image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:27:35.922819image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:28:28.870298image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:29:21.559879image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:30:12.679290image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:31:09.299803image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:32:06.591600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:33:02.672371image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:33:56.323156image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:34:32.685880image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:25:56.403075image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:26:47.103067image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:27:41.186186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:28:34.124562image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:29:26.589943image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:30:17.633803image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:31:14.414822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:32:11.973342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:33:08.055149image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:34:01.520270image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:34:34.927936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:26:00.992385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:26:52.377936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:27:46.573868image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:28:39.302935image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:29:31.629662image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:30:22.525938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:31:19.711257image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:32:16.907926image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:33:13.349326image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:34:06.437074image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:34:37.182278image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:26:05.680304image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:26:57.614513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:27:51.618159image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:28:44.710632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:29:36.648453image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:30:27.975485image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:31:25.033331image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:32:22.180413image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:33:18.614848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:34:11.284963image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:34:39.428685image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:26:10.414286image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:27:02.658660image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:27:56.717508image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:28:49.593996image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:29:41.302659image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:30:32.855491image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:31:30.553920image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:32:27.428818image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:33:24.294479image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:34:15.485649image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:34:41.407212image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:26:12.778730image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:27:04.799207image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:27:59.017056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:28:51.818173image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:29:43.468601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:30:34.991231image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:31:33.070298image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:32:29.891013image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:33:26.819480image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-27T22:34:17.637481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-07-27T22:41:44.010129image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-07-27T22:41:44.105963image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-07-27T22:41:44.200076image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-07-27T22:41:44.407648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-07-27T22:41:44.480455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-07-27T22:35:08.220621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-07-27T22:36:56.484855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-07-27T22:40:04.814029image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-07-27T22:40:50.856495image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

game_idgame_start_timegame_duration_minutesgame_validitygame_featuremod_namegame_total_playersmap_idmap_namemap_widthmap_heightplayer_idplayer_loginplayer_factionplayer_teamplayer_starting_spotplayer_resultplayer_is_computerbefore_ratingafter_ratingleaderboard_ids
0174057202022-06-30 23:59:58 UTC30.7UNKNOWN_RESULTFAF121833.0Hold the line2048.02048.0431007WalgreensUEF31UNKNOWNFalse0.0NaNNaN
1174057112022-06-30 23:59:44 UTC30.2COOP_NOT_RANKEDCoop1NaNNaNNaNNaN96808StarcruiserCybran11UNKNOWNFalse0.0NaNNaN
2174056502022-06-30 23:58:34 UTC48.0VALIDFAF1214570.0Desert Gap1024.01024.0303409ourubapuUEF33DEFEATFalse163.3159.71
3174056502022-06-30 23:58:34 UTC48.0VALIDFAF1214570.0Desert Gap1024.01024.0436692Enzord_RockaUEF211DEFEATFalse686.7692.41
4174056502022-06-30 23:58:34 UTC48.0VALIDFAF1214570.0Desert Gap1024.01024.017943DefiantUEF35DEFEATFalse1082.81078.01
5174056502022-06-30 23:58:34 UTC48.0VALIDFAF1214570.0Desert Gap1024.01024.0359511HalerAeon28DEFEATFalse792.7798.21
6174056502022-06-30 23:58:34 UTC48.0VALIDFAF1214570.0Desert Gap1024.01024.0258970Envoy666Cybran32DEFEATFalse1045.61040.51
7174056502022-06-30 23:58:34 UTC48.0VALIDFAF1214570.0Desert Gap1024.01024.0332940SinidasAeon212DEFEATFalse750.0754.61
8174056502022-06-30 23:58:34 UTC48.0VALIDFAF1214570.0Desert Gap1024.01024.0356873SPACER-BTSeraphim29DEFEATFalse1045.91051.61
9174056502022-06-30 23:58:34 UTC48.0VALIDFAF1214570.0Desert Gap1024.01024.0248696McLeodCybran210DEFEATFalse778.6783.91

Last rows

game_idgame_start_timegame_duration_minutesgame_validitygame_featuremod_namegame_total_playersmap_idmap_namemap_widthmap_heightplayer_idplayer_loginplayer_factionplayer_teamplayer_starting_spotplayer_resultplayer_is_computerbefore_ratingafter_ratingleaderboard_ids
322001735472012-02-21 21:44:01 UTC19.2TOO_MANY_DESYNCSFAF481.0Island Zero512.0512.013942LunagirlCybran32UNKNOWNFalse241.7NaNNaN
322001745352012-02-21 21:36:34 UTC36.5TOO_MANY_DESYNCSFAF6136.0IsisFields3v3512.0512.011364LeibnitzCybran25UNKNOWNFalse1210.3NaNNaN
322001755352012-02-21 21:36:34 UTC36.5TOO_MANY_DESYNCSFAF6136.0IsisFields3v3512.0512.012321tiibAeon34UNKNOWNFalse1069.3NaNNaN
322001765352012-02-21 21:36:34 UTC36.5TOO_MANY_DESYNCSFAF6136.0IsisFields3v3512.0512.06526zanarenoAeon21UNKNOWNFalse1263.8NaNNaN
322001775352012-02-21 21:36:34 UTC36.5TOO_MANY_DESYNCSFAF6136.0IsisFields3v3512.0512.0673DK_SkoldkopSeraphim36UNKNOWNFalse1196.8NaNNaN
322001785352012-02-21 21:36:34 UTC36.5TOO_MANY_DESYNCSFAF6136.0IsisFields3v3512.0512.013250ReDgOUEF32UNKNOWNFalse621.7NaNNaN
322001795352012-02-21 21:36:34 UTC36.5TOO_MANY_DESYNCSFAF6136.0IsisFields3v3512.0512.06529mogwai2000UEF23UNKNOWNFalse1104.9NaNNaN
322001805362012-02-21 21:29:33 UTC15.4TOO_MANY_DESYNCSFAF3181.0Sludge2512.0512.010257jarekAeon12UNKNOWNFalse1061.4NaNNaN
322001815362012-02-21 21:29:33 UTC15.4TOO_MANY_DESYNCSFAF3181.0Sludge2512.0512.010259kipexSeraphim11UNKNOWNFalse895.3NaNNaN
322001825362012-02-21 21:29:33 UTC15.4TOO_MANY_DESYNCSFAF3181.0Sludge2512.0512.010255mat123456Seraphim13UNKNOWNFalse989.8NaNNaN